Minimize population exposure to vehicle-generated emissions by road pricing

Abstract

Vehicular emissions, as a major source of urban air pollution, are threatening public health. With a goal to regulate population exposure to vehicular emissions, three bilevel road pricing models are established to minimize the exposure to vehicular emissions for on-road travelers, near-road residents, and all populations in the study network. A simulated annealing-based method is applied to solve these models based on a first-best pricing scheme. For ease of implementation, a second-best pricing scheme is then proposed and solved based on a meta-heuristic approach coupled with genetic algorithm with elitism and simulated annealing. The proposed method is tested using the Nguyen-Dupuis network and the Sioux Falls network. It is found that a tradeoff exists between the objectives of minimizing the emission exposure levels of travelers and near-road residents. Another tradeoff is found between the objectives of minimizing total population exposure and total travel time. Results from the numerical example indicate that the optimality results obtained in the first-best pricing scheme may also be approximated by the second-best pricing scheme with fewer tolling locations and less toll collection. The conclusions drawn from the paper could potentially assist policymakers in formulating road pricing policies that prioritize environmental concerns.